2D array indexing (row, col) in NumPy - Mini Project: Build & Apply

2D Array Indexing with NumPy

📖 Scenario: You are working with a small grid of temperature readings from different sensors arranged in rows and columns. You want to access specific readings by their row and column positions.

🎯 Goal: Learn how to create a 2D NumPy array and access its elements using row and column indexes.

📋 What You'll Learn

Create a 2D NumPy array with exact values

Define variables for row and column indexes

Use these indexes to access the correct element in the array

Print the accessed element

💡 Why This Matters

🌍 Real World

Accessing specific data points in grids or tables is common in weather data, images, and spreadsheets.

💼 Career

Data scientists often need to extract and analyze specific values from multi-dimensional data arrays.

1

Create a 2D NumPy array

Import NumPy as np and create a 2D NumPy array called temps with these exact values: [[23, 25, 22], [21, 24, 20], [19, 22, 23]].

NumPy

import numpy as np
# Create the 2D array called temps with the given values
# Your code here

Hint

Use np.array() to create a 2D array from a list of lists.

2

Set row and column indexes

Create two variables called row and col. Set row to 1 and col to 2 to select the second row and third column.

NumPy

import numpy as np
temps = np.array([[23, 25, 22], [21, 24, 20], [19, 22, 23]])
# Set row to 1 and col to 2
# Your code here

Hint

Remember that Python uses zero-based indexing, so row 1 is the second row.

3

Access the element at the given row and column

Use the variables row and col to access the element in temps at that position. Store this value in a variable called selected_temp.

NumPy

import numpy as np
temps = np.array([[23, 25, 22], [21, 24, 20], [19, 22, 23]])
row = 1
col = 2
# Access the element at temps[row, col] and store in selected_temp
# Your code here

Hint

Use temps[row, col] to get the element at the specified row and column.

4

Print the selected temperature

Print the value stored in selected_temp.

NumPy

import numpy as np
temps = np.array([[23, 25, 22], [21, 24, 20], [19, 22, 23]])
row = 1
col = 2
selected_temp = temps[row, col]
# Print the selected_temp value
# Your code here

Hint

Use print(selected_temp) to display the value.

Practice

1. What does arr[2, 1] mean when arr is a 2D numpy array?

easy

A. Access the element in the 3rd row and 2nd column

B. Access the element in the 2nd row and 3rd column

C. Access the entire 2nd row

D. Access the entire 1st column

Solution

1. Step 1: Understand zero-based indexing

   In numpy, counting starts at 0, so index 2 means the 3rd row, and index 1 means the 2nd column.

2. Step 2: Interpret the indexing

   arr[2, 1] accesses the element at row 3, column 2.

3. Final Answer:

   Access the element in the 3rd row and 2nd column -> Option A

4. Quick Check:

   Row 2, Col 1 means 3rd row, 2nd column [OK]

Hint: Remember: rows and columns start counting at zero [OK]

Common Mistakes:

• Confusing row and column order
• Starting count from 1 instead of 0
• Thinking arr[2,1] returns a row or column

2. Which of the following is the correct syntax to access the element in the 1st row and 3rd column of a numpy 2D array arr?

easy

A. arr[1, 3]

B. arr[0, 2]

C. arr[3, 1]

D. arr[2, 0]

Solution

1. Step 1: Recall zero-based indexing

   The 1st row is index 0, and the 3rd column is index 2.

2. Step 2: Match indexes to syntax

   So, arr[0, 2] accesses the element in the 1st row and 3rd column.

3. Final Answer:

   arr[0, 2] -> Option B

4. Quick Check:

   Row 0, Col 2 = 1st row, 3rd column [OK]

Hint: Count rows and columns from zero for correct indexing [OK]

Common Mistakes:

• Using 1-based indexes like arr[1,3]
• Swapping row and column indexes
• Using parentheses instead of brackets

3. Given the numpy array:

import numpy as np
arr = np.array([[5, 10, 15], [20, 25, 30], [35, 40, 45]])
print(arr[1, 2])

What is the output?

medium

A. 30

B. 25

C. 20

D. 45

Solution

1. Step 1: Identify the element at row 1, column 2

   Row 1 is the second row: [20, 25, 30]. Column 2 is the third element in that row, which is 30.

2. Step 2: Confirm the printed value

   The code prints arr[1, 2], so the output is 30.

3. Final Answer:

   30 -> Option A

4. Quick Check:

   arr[1,2] = 30 [OK]

Hint: Remember row then column to find the element [OK]

Common Mistakes:

• Choosing element from wrong row or column
• Confusing zero-based indexing
• Reading the array incorrectly

4. What is wrong with this code snippet?

import numpy as np
arr = np.array([[1, 2], [3, 4]])
print(arr[2, 0])

medium

A. Prints 3 correctly

B. SyntaxError due to wrong brackets

C. IndexError because row 2 does not exist

D. Prints 1 correctly

Solution

1. Step 1: Check array shape and indexes

   The array has 2 rows (index 0 and 1). Index 2 for row is out of range.

2. Step 2: Understand error type

   Accessing arr[2, 0] causes an IndexError because row 2 does not exist.

3. Final Answer:

   IndexError because row 2 does not exist -> Option C

4. Quick Check:

   Row index 2 is invalid for 2-row array [OK]

Hint: Check array shape before indexing to avoid errors [OK]

Common Mistakes:

• Assuming indexing starts at 1
• Ignoring array shape
• Confusing error types

5. You have a 4x4 numpy array arr filled with numbers 1 to 16 in row-major order. Which code correctly changes the element in the last row and second column to 100?

hard

A. arr[3, -2] = 100

B. arr[4, 2] = 100

C. arr[-1, 2] = 100

D. arr[3, 1] = 100

Solution

1. Step 1: Identify last row and second column indexes

   For a 4x4 array, rows and columns are indexed 0 to 3. Last row is index 3, second column is index 1.

2. Step 2: Check each option

   arr[3, 1] = 100 uses arr[3, 1] which is correct. arr[4, 2] = 100 uses invalid index 4. arr[-1, 2] = 100 uses column index 2 (third column), not second. arr[3, -2] = 100 uses negative index -2 for column which is the third column, not second.

3. Final Answer:

   arr[3, 1] = 100 -> Option D

4. Quick Check:

   Last row = 3, second column = 1 [OK]

Hint: Use zero-based indexes: last row is size-1, second column is 1 [OK]

Common Mistakes:

• Using out-of-range indexes like 4
• Confusing negative indexes for columns
• Mixing row and column positions